Effective Ways for Marketers for YouTube Targeting

Effective Ways for Marketers for YouTube Targeting

  06:05    Silver Push

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YouTube is the most popular video hosting website in the world with most extensive catalog of online videos. The number of monthly logged-in users on YouTube is about 2 billion. YouTube offers tremendous opportunities to advertisers.

YouTube provides a wide range of ad formats and varied targeting options to marketers, enabling them to effectively and easily reach their target audience. It is important for marketers to use the right YouTubetargeting options in order to drive success to their YouTube advertising campaigns. Using basic keyword and topic targeting at the start may not result in success. Below are discussed some of the hand-picked ways that you as a marketer can use for targeting on YouTube -

Custom Intent Audiences

This targeting option helps marketers in reaching new customers on YouTube on the basis of the keywords used by the users to search for products and services on Google.com. It is not necessary that these audiences have any previous interaction with your brand. These are built from users who have recently searched the keywords that were used by you for to creating your audience.

Some of the great custom intent audiences that you can test out for your video advertising campaigns on YouTube are -

·       Converting search queries - You can use your list of converting queries from your search campaigns to display video ads to users who have searched for these queries.

·       Converting keywords – These keywords are not the same as converting search queries. You can create a different audience based on these.

·       Competitor terms - These allow you to show your video ads to users that are actively searching for your competitors. 

·       Best-selling products - You can create an audience from your best-selling products. You can easily find these products from the sales report of your e-commerce platform. 

Life Events

Life Events can prove to be a great targeting option. It allows you to show ads to customers during life milestones such as starting a business, graduating from college, changing or starting a job, getting married, purchasing a house, retiring from job, etc.

These life events provide a great opportunity to brands, which offer products or services needed in these moments, to emotionally connect with consumers. For effective targeting via life events, your video creative should clearly show consumers how your brand can help them go through these milestones smoothly.

TrueView Discovery Ads

TrueView discovery ads appear on the search results and watch pages on YouTube. YouTube is a huge search engine, next only to Google.com. It allows users to conduct search for specific videos that they are interested in. TrueView discovery ads campaign is unique as a video advertising campaign as it is the only one in Google Ads that allows marketers to target just the YouTube's search results page.

Combining keyword targeting with TrueView discovery campaigns offers a great way to capitalize on user intent. By running TrueView discovery ad campaigns, marketers can take over the top spot of the search results page on YouTube.

Computer Vision-Powered Contextual Targeting

This is a highly effective YouTube targeting method that enables marketers to dramatically boost the performance of their YouTube video advertising campaigns. Computer vision enables AI advertising platforms to detect and understand contexts in online videos. Faces, emotions, logos, objects, activities and scenes in online videos can be detected with high accuracy.

By using computer vision powered contextual targeting, you can place your ad against the video content that is highly relevant to the ad, i.e. your ad is fully in line with the content the user is actively engaging with. As the ad shown matches the current interest of the user, the chances of user viewing or clicking the ad are very high.

AI contextual targeting powered by computer vision offers a very high degree of contextual relevance unmatched by other methods of contextual advertising such as keyword targeting, which fail to fully reflect the user’s current state of mind and cannot understand nuances in context.

The above-mentioned, hand-picked ways for YouTube targeting will help marketers in effectively achieving their YouTube advertising goals.    

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Beyond Black and White: The True Color of Brand Safety

Beyond Black and White: The True Color of Brand Safety

  23:02    Silver Push

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Over the past few years, a lot of brand safety issues have surfaced that have led marketers to review their brand safety measures. The current coronavirus crisis has intensified the brand safety woes of marketers, as most of the brands don't want ad adjacency to the content dealing with morbidity and mortality. 

Common brand safety methods used by marketers include blacklisting and whitelisting. Blacklisting involves avoiding placement of ads against content containing one or more blocked keywords. In case of video content, a blocked keyword is searched in topic, title, description and metadata.

Keyword-based blacklisting method is in reality not that effective as it seems to be. It is marred by under- and over-blocking of content. Research shows that because of the use of keyword blacklists, more than half of the safe stories published on the major news platforms are being incorrectly tagged as brand unsafe.

Keyword-based blacklisting method can lead to blocking of completely innocuous content. This is because it fails to comprehend the nuances in context, i.e. it is unable to understand the true context in which a keyword is used. For example, if "alcohol" is the blocked keyword, then the blacklisting method will not only tag a video featuring drunk and driving as unsafe, but will also tag a video featuring a recipe in which alcohol has been used as one of the ingredients, as unsafe.

Another problem with blacklisting is that universal blacklists cannot be created. They have to be regularly updated and modified according to the brands' requirements, current happenings and events, latest news, countries, languages and culture. There is also a requirement to tweak blacklists regularly on the basis of current safe content consumption patterns of consumers, so that increased reach for the advertising campaigns can be achieved. Overall, keyword-based blacklisting method is quite cumbersome to implement as it needs a lot of fine-tuning. With this method, content under- and over-blocking is a common problem, and this hinders marketers in getting optimal results from their advertising campaigns.

A whitelist enlists content that has been labeled as safe for ads to be placed against it. A whitelist provides a safe and trusted environment to brands to advertise within. Curating a whitelist for advertising on a video platform, for example for YouTube advertising, involves tagging unsafe content at the keyword, topic, video and channel levels. Video-level tagging helps brands to filter out unsafe videos from an otherwise safe channel; brands do not have to blacklist the entire channel just because of one or few unsafe videos.

Again, like keyword blacklists, whitelists need to be regularly updated, otherwise the campaigns will not witness an increase in reach, and brands will miss newer safe and engaging content for their ads; ads will keep displaying against the same video content enlisted in the static whitelist. 

Creation of whitelists is not an easy process; it requires a lot of curation by marketers, and is time-consuming and expensive. As the whitelisting method limits the number of videos against which ads can be placed, marketers are unable to take the full advantage of the true potential of huge video hosting platforms like YouTube. The campaign's reach gets reduced and the right audience does not get fully targeted.

The above-mentioned brand safety methods provide only suboptimal brand safety and have significant limitations. A highly effective way of ensuring brand suitability and safety is provided by contextual brand safety method that makes use of AI and computer vision. AI-powered brand safety platforms that deploy computer vision technology, provide high degree of context relevance unmatched by keyword-based methods.

Computer vision can accurately detect contexts in videos such as faces, objects, logos, on-screen text, emotions, scenes and activities. Thus, it can effectively detect unsafe or harmful contexts in videos without the risk of under- and over-blocking of content.

Amid the coronavirus pandemic, computer vision-powered brand safety platforms enable brands to selectively block ads against mortality-related coronavirus content, while allowing ad placement against positive coronavirus content. Thus, brands can safely capitalize on the news content; this is not possible with keyword-blacklists that fail to understand the true context in which the keyword "coronavirus" is being used.

By using AI-based contextual brand safety method, marketers can not only effectively block ad placement against recognized unsafe categories, but can also custom define unsuitable contexts that are unique to a brand. This helps them provide a fully suitable environment to brands for advertising.         

Computer vision enables marketers to go beyond blacklists and whitelists in order to achieve brand safety in its true color.    

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Synergistic Approach to Visual Content Moderation Is Both Effective and Efficient

Synergistic Approach to Visual Content Moderation Is Both Effective and Efficient

  02:49    Silver Push

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Enormous amount of content in the form of images, videos and text is posted on the world wide web on an hourly basis. As this content is posted by users around the globe, the nature of the content is highly heterogeneous.

User-generated content carries an immanent risk of being inappropriate, harmful, offensive, or dangerous. This content can be classified into the categories such as nudity, terrorism, hatred, child exploitation, violence, misinformation, etc. and requires strict moderation.

Content moderation is commonly achieved through human moderators. AI-based content moderation has also emerged and offers an automated way to filter out inappropriate content.

The enormous and heterogeneous user generated content cannot be moderated effectively and efficiently by using just one method of moderation - manual or automatic. The best approach is synergistic, i.e. using the combination of both human and AI moderation. Social media platforms are increasingly using the synergistic approach for achieving optimum level of content moderation.   

By using the synergistic approach for content classification and moderation, online platforms can enjoy the benefits of both human and AI moderation - the intelligence, wisdom and judgement of human beings, and the capability of AI-powered platforms to evaluate enormous amount of content in no time.         

AI content moderation platforms powered by computer vision makes image and video moderation highly efficient. Computer vision can detect faces, emotions, objects, logos, on-screen text, actions and scenes in the images and videos with high accuracy. Such platforms can determine whether the images or videos should be reviewed by a human content moderator or not. Thus, human moderators are saved from filtering out large volumes of content themselves; this also saves them from viewing mentally disturbing content in large quantities on a daily basis. They can look only at the images and videos flagged by the AI platform and make a publishing decision. The decision taken by the human moderator feeds back into the algorithm, but the reason for the decision does not.    

AI makes content moderation much easier for human moderators. By considering a number of factors, an advanced AI content moderation algorithm can calculate a relative risk score to determine if a user's post should be posted immediately after creation, reviewed before posting, or should not be posted. This relative score can then be used by human moderators while making a publishing decision.

Although AI content classification and moderation enables online platforms to hire less number of human moderators, the need for human moderation will always remain and is indispensable. Without human moderators, accurate content moderation is not possible. Only human content moderators can make decisions that lie in the gray areas of decision-making, view a user's content from a subjective perspective, understand cultural context of content, etc.

Armed with a computer vision powered video and image moderation platform, human content moderators easily identify and filter out inappropriate visual content from large volumes of user generated content posted on online platforms.

By following a synergistic approach, which involves using both AI and human moderation, online platforms dealing with loads of user generated content can achieve efficient and effective content moderation.

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Which Is Better for Your Business – Manual or AI Visual Content Moderation?

Which Is Better for Your Business – Manual or AI Visual Content Moderation?

  02:55    Silver Push

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Visual content moderation is important for businesses or brands, especially if they have to deal with a lot of user-generated visual content. Any association with inappropriate content can damage their reputation, weaken consumer trust and result in decrease in sales.

Traditionally, visual content classification and moderation has been done manually. But with the advent of AI, automated content moderation platforms have emerged. These platforms make use of computer vision and provide an effective way for image and video classification and moderation.       

Whether a brand or business should moderate visual content manually, use AI-powered automated content moderation or augment manual moderation with an automated one, depends on a number of factors. These factors are discussed here below –

Source of content

In order to build brand recognition and consumer trust, more and more brands are now allowing user-generated content on their own platforms. However, user-generated content is potentially risky and can include inappropriate matter that can be highly damaging for the brands. Although brands can dictate their content posting guidelines to users, they do not have actual control over what a user is posting. Moderating such content is a must for brands. As there are high chances of user-generated visual content being inappropriate or unsuitable, brands should opt for computer vision-powered video and image classification and moderation platform.

If in case, most of a brand’s visual content is not user-generated, but is sourced internally or from highly trust-worthy third parties, then for such a brand, video and image moderation can be performed manually by hiring human content moderators and there is a lesser need for an automated system.

Volume of content

For brands that have to deal with a good volume of visual content, especially user-generated content, manual moderation does not work effectively and efficiently. They should make use of computer vision-powered image and video moderation platforms.

AI-powered systems can tackle enormous content volume with a high degree of accuracy. Computer vision technology effectively classifies and tags visual content at scale. Such automated systems are not plagued by human errors, can work continuously unlike human beings, and their algorithms get self-trained from the data they handle.

Nature of content 

An automated AI content moderation platform can effectively filter out content such as “not safe for work” images and videos, and other forms of inappropriate, offensive or dangerous content, but it falls short when it comes to filtering out misinformation. Here, human intervention from human content moderators is required.

User-generated visual content can be highly mentally disturbing for human content moderators. Filtering out such content through automated computer vision powered content classification and moderation platform is the best way to prevent ill effects on mental health.  

Hiring a large number of human moderators is quite expensive and may not be feasible for businesses with small budgets. Also, in most of the cases, as discussed above, manual moderation is less effective than computer vision powered visual content moderation.

For brands or businesses that have to handle a large amount of user-generated visual content, computer vision-based content moderation is much better than manual moderation in terms of accuracy, effectiveness and efficiency.

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Using Computer Vision for Effective Visual Content Strategy

Using Computer Vision for Effective Visual Content Strategy

  04:04    Silver Push

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Visual formats such as images and videos are embraced by people over just a plain piece of text. Images and video enable brands to bring life to their messages, making consumers better understand their products and services. For brands, an effective and strong visual content strategy drives engagement and sales.

Research shows that brands are using visual formats much more on their own platforms and their social media pages for conveying messages to consumers, but less frequently in display ads.

But what is causing marketers to give less preference to display ads when it comes to using highly effective content formats - images and videos - for communication with the consumers? Research shows that using their own platforms allow them to exercise more control over their visual content in comparison to putting it out on the uncontrolled internet in the form of ads. There is enormous competition and it is hard for marketers to ensure that they are reaching their targets and drawing user engagement.

Another reason that marketers cite is of brand safety. Enormous amount of content is uploaded on the internet on daily basis and marketers have no idea against what content their ads would get displayed. On their own platforms, whole content is under their control.

Research shows that when it comes to using visual content for increasing user engagement, raising brand awareness and generating revenue, marketers face the following issues – insufficient viewability, contextual irrelevance, and ineffective demographic targeting. Data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), along with the gradual phasing-out of third-party cookies in Chrome by Google, have made practices like demographic targeting all the more difficult. 

The problems that hinder the use of visual formats by marketers in display advertising, namely – insufficient control over ad placement, insufficient user engagement, brand unsafe environment and data privacy laws – can be resolved through contextual targeting.

Contextual targeting involves placement of an ad against the content that is relevant to the ad, i.e. the ad is in line with the content that the user is currently interested in. Contextually targeted ads readily capture the attention of users and increase their chances of viewing or clicking them, as it is likely that users are already interested in the products or services being advertised.

Keywords-based contextual advertising often delivers sub-optimal results as keywords fail to fully reflect the user’s current state of mind, while AI-powered solutions that utilize technologies such as NLP and semantic analysis fail to understand nuanced contexts and complex relationships that exist between words.

The true contextual targeting can only be achieved through computer vision. By leveraging computer vision, marketers can take control of their visual content strategy and use visual formats to run highly effective video advertising campaigns, without worrying about data privacy and brand safety issues.

Computer vision is an advanced technology that enables computers to understand images and videos. Computer vision uses deep learning to make computers learn how to detect patterns in images and streaming videos.

Computer vision powered contextual advertising technology works by accurately detecting contexts in streaming videos in order to display in-video ads that are in line with what the user is actively engaging with. Any content that is unsafe or unsuitable is contextually filtered out to provide true brand suitability.

Computer vision enables marketers to embrace contextual targeting and fully utilize their visual content for achieving their marketing goals.

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Protecting brand reputation with AI

Protecting brand reputation with AI

  04:27    Silver Push

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We just learned something quite distressing – that one in 10 videos out in the online jungle we call the internet – can contain something potentially ‘harmful’ and ‘damaging’.

What we mean by this is that some videos contain certain elements that may not be accurately reflected by the title or tags associated with it. This poses a problem for brands who may not want to be associated with adult themes or extreme violence.

To find out what this means for brands and what options they have, we spoke to Kartik Mehta, Chief Revenue Officer, SilverPush. With their new product Mirrors Safe, the brand offers an AI-Powered context-relevant brand suitability platform to help prevent unwanted associations.

In order to build this product and understand they extent of the issue, Silverpush reviewed 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. With one in 10 or 10% containing images or negative associations (according to Silverpush criteria), there is definitely a need for a solution.

Congrats on the launch of Mirrors Safe. How do you see this new product helping brands and their advertisements?

Brands today are faced with different types of risks – financial risks, legal risks, and I guess the most important part of it is the reputation risk which could have larger concerns and probably a long-lasting impact on the overall brand equity. The existing brand safety measures like blocklists and whitelists are primarily focused on the principle of exclusion, which does protect brands to an extent but can also lead to one of the most pressing brand safety related concerns of over-blocking. Which can lead to brands missing the opportunity of engaging with the audiences across the right kind of content. 

Whereas Mirrors Safe’s computer vision powered in-video context detection identifies faces, actions, scenes, emotions, on-screen-text in a streaming video to detect content that features violence, smoking, nudity, arms & guns and more. It detects these contexts only when they feature in a video, and not just by relying on keywords used to describe the video – which often times are misleading and can result both in unsafe placements as well as over blocking. 

For instance, a video featuring smoking or violence might not be described so in its title, description or meta tags. There is no way for keyword-based solutions to identify these damaging contexts to filter out this video. Which can lead to household brands advertising across content which is highly unsuitable for their brand image. On the other hand, keywords like shoot, kill, crash, and even gun (some of the most blocked keywords) can easily be used within perfectly safe contexts, (like movies and songs). 

Moreover, Mirrors Safe’s context detection makes it possible to offer brand suitability that can be customized for each brand or each category without following the blanket exclusion principles. 

According to your research, 1 in 10 videos are deemed to be associated with dangerous or damaging content. How were you able to analyze around 15 million videos to generate this data?

Silverpush churned approximately 15 million videos across the largest video hosting and sharing platforms in the SEA region using Mirrors Safe. We used a randomly chosen inventory across platforms from our existing database – previously used to run campaigns using our video advertising platform Mirrors.  

It was found that nearly 8-9% of analyzed content to be deemed brand unsafe. This means these videos featured one or more unsafe contexts like nudity, smoking, violence, arms and guns, and more. 

However, a bigger discovery was the difference between the results found by exclusion through traditional methods like keyword lists vs. Mirror Safe’s in-video context detection technology.

For instance, when we used both methods to identify unsafe videos for one of the top brand unsafe categories – nudity and adult content, Mirrors Safe (through its frame-by-frame parsing) identified 300% more video content featuring unsafe context in this category, compared to exclusion through keyword lists.

A single damaging ad placement can harm brand perception in the consumer’s mind. This discovery highlights the potential harm that existing traditional measures are unable to detect. This has been witnessed time and time again, with some of the largest video advertising platforms being unable to keep brands safe from damaging content.

Have you been able to measure or estimate the negative impact of these associations for the brand? 

There is already a plethora of information available on how even a single ad placement across harmful content can irreparably damage brand perception for a long time in the consumers mind. A 2019 study Trustworthy Accountability Group & Brand Safety Institute found that 80% consumers will stop or reduce buying products advertised against extreme or violent content. And, 70% believe advertiser and the agency are most responsible for a brand’s ad placements.

With our platform Mirrors, we have been serving contextually targeted video advertising across platforms since 2018. And we identified the challenge posed by traditional brand safety measures while serving our clients. We realized that ensuring brand safety is even more of a challenge across video formats, as NLP based technologies that work for other formats are ineffective in gauging the right context featured in video content. 

Conversations and feedback from partners first led us to introduce a safety feature in Mirrors, where brands working with us did not report a single unsafe exposure since the launch of the feature. This further led us to launch Mirrors Safe, which can be deployed as a standalone context relevant suitability platform. 

How has this solution helped brands so far? Do you have any initial test cases or beta usage that you can share?

I will start with the most interesting use case, that is highly relevant today.  

Helped brands navigate the extreme over-blocking of COVID-19 related content 

As brands and platforms rapidly add terms associated with COVID-19 to their keyword block, Coronavirus has become one of the most blocked keywords today. We have found advertisers looking to avoid unsafe brand exposure around this sensitive topic are forced to exclude news entirely from their list of targeted channels and publishers. However, excluding news and related channels entirely from advertising strategies across platforms is killing reach for brands. 

One of the key factors behind extreme COVID-19 related over-blocking is the inability to detect if the COVID-19 related stories are informative Vs. stories that can harm brands – leading to blanket exclusions. Mirrors Safe identified what the video content features to differentiate the stories, in the following ways: 

• On-screen text recognition: Mirrors Safe identifies and filters out videos that have related text written on the screen (e.g. Coronavirus or COVID-19). And can help differentiate between morbidity related stories Vs. more positive stories around for instance precautions. 
• Object and action detection: the system can identify objects like masks, stretchers, and actions like coughing and sneezing and understand the concentration of this content within a single video through frame by frame parsing.
• Faces: with this outbreak certain public figures are also on brands’ blocklists (yes, Trump). In-video context detection can accurately identify faces to filter out related content.

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Silverpush Launches AI-Powered Brand Suitability Platform – Mirrors Safe

Silverpush Launches AI-Powered Brand Suitability Platform – Mirrors Safe

  04:48    Silver Push

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Mirrors Safe uses computer vision to provide unequaled brand suitability for in-video ad placement.

Singapore, 20 May 2020: Silverpush has today announced the launch of its new AI-powered brand suitability platform – Mirrors Safe. Silverpush is well-acknowledged for its AI-powered contextual video advertising and real-time moment marketing products that enable brands to achieve unprecedented reach and user engagement.

Brand safety poses a serious risk to brands. Research shows that 80% of customers will not buy products at all or reduce their buying of products from brands that place ads across any type of harmful or offensive content. 70% of the customers hold the brand or agency for hurtful ad placement.

By using computer vision to detect contexts in video, Mirrors Safe overcomes the limitations of conventional brand safety methods such as keyword-based blacklists and whitelisted channels. It accurately detects contexts in videos such as faces, objects, logos, emotions, scenes and activities and filters out harmful content across a broad range of brand unsafe categories including terrorism, violence, nudity, hate speeches, smoking, etc.

Mirrors Safe makes use of an advanced algorithm for calculation of brand suitability score. This comprehensive score takes into account five parameters. This score measures safety and suitability of the content, page and channel. The five parameters are –

• Engagement: likes, dislikes & participation that the content generates
• Safety: exclusion through in-video context detection, on-screen text, and audio sentiment analysis
• Influence: organic influence that channel/page/content creates
• Relevance: how relevant is the content in terms of its peer channel/page category
• Momentum: consistency that channel/page maintains or grows in terms of engagement

Silverpush’s CRO, Kartik Mehta, said: “What sets Mirrors Safe apart is its ability to custom define the scope of harmful contexts, that are unique to every brand. Thus, helping brands move beyond just brand safety to a truly brand suitable environment. This is limited with existing keyword and natural language processing (NLP) based blanket exclusion technologies, as these often fail to understand the complex undertones and various contexts words can be used for”.

Silverpush used Mirrors Safe to analyze about 15 million videos across the largest video hosting and sharing platforms in the South East Asia region using Mirrors Safe. The analysis found 8% to 9% of the video content as brand unsafe, i.e roughly 1 in 10 videos has some type of brand damaging content.

Silverpush compared traditional brand safety measures with Mirrors Safe to identify nudity and adult contents in videos. Result was amazing as Mirrors Safe identified 300% more unsafe videos compared to conventional brand safety methods.

This finding brings into light the inefficacy of the traditional brand safety measures and the potential harm they can do to a brand’s image. The use of traditional measures has led to serious brand safety issues for some of the biggest video platforms.

“Mirrors Safe further addresses one of the most pressing brand safety challenges of content over-blocking – a result of blanket exclusion measures offered today. This significantly limits campaign performance and often forces marketers to switch off controls in favor of reach. Mirrors Safe’s in-video context detection technology prevents over-blocking and only filters videos that actually feature unsafe contexts, ensuring brand safety without hampering monetization and performance” – Mehta added.

Visit silverpush.co/mirrors-safe/ to know more about Mirrors Safe.

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  01:56    Silver Push

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How are brands responding to COVID-19? A brand marketer survey across SEA market

Brands have been profoundly affected by the coronavirus pandemic. Brands’ response to the coronavirus pandemic not only impacts consumers’ trust today, but it will also significantly impact future purchasing decisions. Moreover, brands could face irreparable damage to their reputation due to brand safety risks associated with COVID-19 related content.

To gain insight into how brands are responding to COVID-19 pandemic, Silverpush conducted a survey of 150+ agency heads, business leads in media, and brand marketers in the SEA region in April 2020.

The survey aimed to understand how brands are adapting their marketing strategies to the impact of the COVID-19 outbreak and how they are mitigating the very real brand safety risks the rapidly growing coronavirus related content consumption poses.

How are brands re-imaging and engaging consumers in light of the pandemic?

The survey found that in the light of the pandemic, brands are reimaging by adapting their marketing tone and initiatives to consumer expectations. Only 5% respondents reported no change in brand positioning pre and post COVID-19, whereas 95% reported a distinct shift that resonates with government policies, and responds to the new consumer expectation.

Ad spending poised to decline

The industries heavily impacted by coronavirus outbreak such as travel, hospitality, physical retail and more have and will continue to paused marketing initiatives. Only 16% respondents said these industries will protect marketing budgets for a stronger comeback later.

Moreover, the survey indicates that it is unlikely that the industries such as health and FMCG that are currently experiencing higher demand will increase marketing spend to capture the demand more aggressively. Even though past recessions have shown that aggressive cuts in ad spends can lead to longer recovery cycles.

Ad Spends are shifting to digital channels

Even with significantly increased TV viewership across SEA, boosted due to government-imposed lockdowns across the region, and various studies indicating curtailed TV ad spends can adversely affect brand health measures - only 2% respondents said brands are spending more on TV and mainstream media, and a large percentage indicated rapid shift to various digital channels.

Brand safety is a key concern, and is driving ad spend cuts

Industries, except few such as health, hygiene, pharma, etc., are stringently avoiding advertising across COVID-19 related content. Publisher news sites and news channels on platforms like YouTube are facing advertisers’ block-lists due to coronavirus-related coverage.

A measure of advertisers’ confidence on brand safety tools is depicted by how despite using third party tools to ensure safe ad placements, brands are reducing marketing budgets and pausing advertising specifically to avoid association with Coronavirus related content.

71% respondents reported brands are reducing marketing budgets ranging from complete halt of marketing spends leading to up-to 80% budget cuts, in order to avoid running ads across coronavirus related content

Can context relevance be the answer?  

Emerging AI powered solutions are increasingly focusing on providing context relevance, and are fast becoming an answer to brand safety woes. AI enables processing of large volumes of data at speed, with better context, at higher scale and improved targeting efficiencies.

However, most of these contextual targeting solutions still depend on the use of NLP and semantic analysis, not truly understanding the sub-text, nuanced contexts, and complex relationship words have in written or spoken language.

AI and computer vision-powered video advertising solutions can detect in-video contexts, offering a higher degree of context relevance that surpasses limitations of traditional keyword targeting and NLP based technologies. They offer unparalleled insight for advertisers to place context-relevant in video ads and exclude unsafe content in a highly structured manner, and at the scale programmatic has traditionally offered.

You can access the full report ‘Brand Response to COVID-19 in SEA’ for detailed insights from the survey. 

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  23:28    Silver Push

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Understanding the Post Covid-19 Contactless Workplaces

The coronavirus pandemic has changed many aspects of human lives. Many people are now working from home. The post Covid-19 workplaces will not be the same conventional workplaces that people have been familiar with for years. Workplace safety will take priority over other matters.

The workplaces will undergo a radical shift from touch-based to touchless. Things like fingerprint-based biometric devices, touch-based screens for booking conference and meeting rooms, kiosks for guest check-in, handle-operated doors, etc. will have to be replaced with viable alternatives to prevent the spread of infectious diseases and ensure employee safety.

The contactless workplaces will make use of automation and touchless technology. Normal doors will be replaced by automatic doors, elevators will be voice-controlled, lighting system will adjust brightness automatically according to the time of the day, temperature control system will be adjusted by gestures or voice, and water dispensers will automatically pour water on keeping a bottle or glass below the tap.

The washrooms will have touchless automatic faucets, hand-free soap dispensers, and automatic flush powered by infrared technology. These products will not only reduce spread of germs, but also save water and soap. Along with touchless hand dryer, touchless paper towel dispenser will also be provided.

The contactless workplaces will make use of contactless attendance system powered by facial recognition technology in place of fingerprint-based biometric attendance system. A computer vision powered face-recognition-based attendance system can easily recognize the faces of employees for the purpose of attendance. Computer vision is an advanced field of artificial intelligence that enables computers to see like human beings and easily identify visual content in images and videos.

Besides powering the face recognition biometric systems, computer vision powered solutions can help employers ensure wearing of face masks by employees and enforce workplace social distancing and sanitization compliance. This technology can also bring into notice if an employee coughs or sneezes.

As an employee health and safety measure, workplaces will have to make use of touchless temperature recording technology. This technology will work by using thermal sensors for recording temperature of both employees and visitors. If any anomaly is detected, it will be instantly reported to the concerned department.

Post Covid-19 workplaces will not allow sharing of accessories such as headphones and each employee will be provided individual accessories along with laptops or desktops. Professional cleaning and sanitization protocols will be regularly implemented for workstations, devices, conference and meeting rooms, reception area, cafes, etc. Easy access to hand sanitizers will be provided throughout the workplace for both employees and visitors. The sensor-based touchless garbage bins will provide a safe way to dispose of garbage.

Workplaces will incorporate antimicrobial materials into interior design elements such as wall paints, door sheets, window shades, etc. Such materials will resist the growth of microbes, thus providing a cleaner surface. To ensure workplace social distancing, workplaces will follow a de-clustering approach by keeping individual employee desks wide apart from each other. This can be achieved by using a larger office area or by creating branch offices. Encasing individual desks will provide added protection from transmission through respiratory droplets. Post Covid-19 workplaces will require advanced air filtration technology in order to effectively filter out disease causing microorganisms.

The post Covid-19 contactless workplaces, by making use of advanced technologies, will help employers to carry out their business, while ensuring employee health and safety.

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  22:06    Silver Push

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Top Applications of Face Recognition Technology

The work on face recognition technology started decades ago, but only recently this technology has achieved widespread use. A facial recognition system is used to identify a person from his face. A person can be identified when he is present physically, or from his photograph or video.

Once face detection and analysis was considered a part of science fiction. But, now with the introduction of this technology in the smartphones, many people have become well acquainted with its use. There are many use cases deploying facial recognition technology, some of them are given here below:     

Access control

Whether it is about having access to a smartphone, or to a building, or crossing a country’s border, facial recognition technology is there to make it as secure as possible. By deploying this technology places such as a school, workplace, residence, etc. become highly secure as only authorized persons can enter into the premises.

Along with sensor-based automatic doors, face recognition allows touchless entry and exit for employees. This will enable employers to ensure employee health and safety at post Covid-19 contactless workplaces.

Crime prevention and identification of criminals

Facial recognition technology is deployed for conducting police checks. In the U.S, law enforcement agencies use this technology to run searches against licensed drivers’ database. To identify a suspect in a huge crowd, a large aerial camera fitted on a drone and connected to a face detection system can be used. In retail outlets, this technology can identify a person with a history of shoplifting right at the time when he is entering the premises.

Facial recognition-based CCTV systems can be used to find missing children, victims of human trafficking, and criminals. In 2018, Delhi police identified 2930 missing children while test running a new facial recognition software.

Attendance tracking

Although fingerprint-based biometric attendance systems have proved to be effective at workplaces, they carry an inherent risk of transmission of contagious diseases such as Covid-19. Being touch-based, they can easily transfer viruses and bacteria from one person to another. Face recognition attendance systems, powered by computer vision, work in a contactless manner, thus providing an edge over the fingerprint-based systems. They will help prevent spread of infectious diseases at post Covid-19 workplaces.

Video advertising

Computer vision powered face detection has revolutionized the video advertising industry. By recognizing faces of the characters in the online videos, this technology enables placing of in-video ads that are in line with what a user is watching. Besides faces, computer vision technology can easily identify emotions, objects, scenes and activities in video. This advanced form of in-video contextual advertising is highly effective, allowing brands to achieve unprecedented reach and user engagement.   

Health

Face recognition has been used to diagnose diseases. A face detection software has been used by the researchers at the National Human Genome Research Institute (NHGRI) in the United States to successfully diagnose a rare, genetic condition known as DiGeorge syndrome. Facial analysis has made it possible to track medication use by a patient in a more accurate manner. This technology has also been used in the assessment of pain levels in order to support pain management.

From contactless attendance to video advertising, there are varied uses of the face recognition technology. More of its use cases will surface in the near future as this technology is progressing at a fast pace.

M-Shield, developed by Silverpush, is an AI-powered facial recognition-based attendance, access management and human monitoring system. This social distancing platform makes workplaces and public spaces safe by preventing the spread of contagious diseases such as Covid-19.

M-Shield makes use of facial recognition technology, powered by computer vision, for contactless attendance, entry/exit access management, and mask and social distancing compliance. Its touchless attendance tracking system accurately identifies the faces of employees, even if they are wearing masks. It ensures mask compliance and detects whether the mask is properly worn or not. M-Shield ensures workplace social distancing by tracking minimum distance requirements between employees. Its contactless temperature monitoring technology detects any anomaly in body temperature. It offers added safety by generating an alert if someone coughs, sneezes, or do a handshake.

M-Shield will help employers re-introduce workforce back into offices while ensuring employee health and safety, and compliance with Covid-19 related policies. It will enable government to ensure public safety, when the Covid-19 lockdown lifts.

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